Many enterprises collect large amounts of facts on which they can base business decisions. For example, the facts may be contained in records that are “cookies,” created by a browser as a result of particular actions of users with respect to web pages being processed by the browser. The facts may be characteristics of the particular actions such as, for example, which pages of a particular web site a user has visited. While these facts provide much information about the users' behavior, it can be difficult to process so many facts in order to glean the useful information, in order to make a particular business decision.
An “aggregation-type operation” may be performed to distill a large amount of facts (such as the facts contained in cookies) into some aggregate value that represents an aggregate of the large amount of facts, such that a business decision may be made based on the aggregate value. However, when an aggregation-type operation is to be performed on the large amount of facts, it can be very computationally intensive to access and process all of the available facts to accomplish the aggregation-type operation. This computational intensity is further exacerbated when multiple aggregation-type operations are to be performed on the same facts.